Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark

• URL: http://arxiv.org/abs/2311.09122v3
• Date: Sat, 29 Jun 2024 22:50:48 GMT
• Title: Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
• Authors: Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, Joseph Marvin Imperial, Börje F. Karlsson, Peiqin Lin, Nikola Ljubešić, LJ Miranda, Barbara Plank, Arij Riabi, Yuval Pinter,
• Abstract summary: We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. UNER v1 contains 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 diverse languages.
• Score: 39.01204607174688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We release the data, code, and fitted models to the public.

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